Exploratory Analysis

Visualize all Observations by State & Species

data(us.cities)

# Get major cities for each sample region (state)
.states <- c("OR", "VT", "CO", "NC")
top.cities <- purrr::map_df(.states, function(s) {
  out <- us.cities %>% 
  filter(country.etc==s) %>%
  mutate(city = gsub(paste0(" ", s), "", name)) %>%
  arrange(-pop)
  if (s == "OR") {
    out <- out %>% 
      head() %>%
      filter(!(city %in% c("Gresham", "Hillsboro", "Corvallis",
                           "Beaverton", "Springfield")))
  } else if (s == "CO") {
    out <- out %>%
      head() %>%
      filter(!(city %in% c("Thornton", "Lakewood", "Aurora")))
  } else if (s == "NC") {
    out <- out %>%
      head() %>%
      filter(!(city %in% c("Greensboro", "Durham", "Fayetteville")))
  } else {
    out <- out %>% head()
  }
  out
})

# Load the map data
states <- map_data("state") %>% 
  filter(region %in% c("oregon", "north carolina", "colorado", "vermont"))

# Load your data
data.files <- list.files("data/final", full.names = T)

df <- purrr::map_df(data.files, readRDS) 

caps.after.ws <- function(string) {
  gsub("(?<=\\s)([a-z])", "\\U\\1", string, perl = T)
}

# Define a function to create a plot for each species
plot.for.species <- function(spec, st.abbr) {
  st <- case_when(st.abbr == "CO" ~ "colorado",
                  st.abbr == "NC" ~ "north carolina",
                  st.abbr == "VT" ~ "vermont",
                  st.abbr == "OR" ~ "oregon",
                  T ~ "")
  
  title <- caps.after.ws(paste(st.abbr, gsub("_", " ", spec), 
                             "Observations, 2016-2019"))
  
  p <- ggplot(data = states %>% filter(region == st)) +
    geom_polygon(aes(x = long, y = lat, group = group),
                 fill = "#989875", color = "black") +
    geom_point(data = df %>% filter(state == st.abbr & common.name == spec), 
               aes(x = lon, y = lat), 
               size=1, alpha=.5, fill = "red", shape=21) +
    geom_point(data = top.cities %>% filter(country.etc == st.abbr), 
               aes(x=long, y=lat),
               fill="gold", color="black", size=3.5, shape = 21) + 
    geom_text(data = top.cities %>% filter(country.etc == st.abbr), 
              aes(x=long, y=lat, label=city),
              color="white", hjust=case_when(st.abbr=="NC"~.2,
                                               st.abbr=="VT"~.65,
                                               T~.5),
              vjust=ifelse(st.abbr=="NC", -.65, 1.5),
              size=4) + 
    coord_map() +
    ggtitle(title) +
    theme_minimal() +
    theme(panel.background = element_blank(),
          axis.text = element_blank(),
          axis.title = element_blank(),
          axis.ticks = element_blank(),
          panel.grid = element_blank())

  data.table(
    state=st.abbr,
    species=spec,
    plot=list(p)
  )
}

spec.state <- expand.grid(unique(df$common.name), unique(df$state)) %>%
  rename(spec=Var1, st.abbr=Var2) 

# Create a list of plots
plots <- purrr::map2_df(spec.state$spec, 
                        spec.state$st.abbr, 
                        ~plot.for.species(.x, .y))

# Plot Ruddy Duck plots
do.call(ggpubr::ggarrange, 
        c(plots[species == "Ruddy Duck"]$plot, 
          list(nrow=2, ncol=2)))

# Plot Belted Kingfisher plots
do.call(ggpubr::ggarrange, 
        c(plots[species == "Belted Kingfisher"]$plot, 
          list(nrow=2, ncol=2)))

# Plot Wild Turkey plots
do.call(ggpubr::ggarrange, 
        c(plots[species == "Wild Turkey"]$plot, 
          list(nrow=2, ncol=2)))

# Plot Sharp-Shinned Hawk plots
do.call(ggpubr::ggarrange, 
        c(plots[species == "Sharp-shinned Hawk"]$plot, 
          list(nrow=2, ncol=2)))

# Plot Downy Woodpecker Plots
do.call(ggpubr::ggarrange, 
        c(plots[species == "Downy Woodpecker"]$plot, 
          list(nrow=2, ncol=2)))

# Plot Cedar Waxwing Plots
do.call(ggpubr::ggarrange, 
        c(plots[species == "Cedar Waxwing"]$plot, 
          list(nrow=2, ncol=2)))

# Plot Sandhill Crane Plots
do.call(ggpubr::ggarrange, 
        c(plots[species == "Sandhill Crane"]$plot, 
          list(nrow=2, ncol=2)))

# Plot Sanderling Plots
do.call(ggpubr::ggarrange, 
        c(plots[species == "Sanderling"]$plot, 
          list(nrow=2, ncol=2)))

Explore Explanatory Rasters

states <- c("CO", "NC", "OR", "VT")
r.files <- paste0("data/final_rasters/", states, ".tif")
r.list <- purrr::map(r.files, rast)
names(r.list) <- states

TODO: - terra::freq - terra::density - terra::layerCor

Principal Component Analysis

r.df <- map_df(states, function(s) {
  df <- r.list[[s]] %>% as.data.frame()
  names(df) <- names(df) %>% gsub(paste0("_", s), "", .)
  df %>% setDT()
  df[, state := factor(s, levels=states)]
  df[apply(df, 1, function(.x) !any(is.na(.x)))]
}) 

# Custom function to process factor levels
clean.levels <- function(x) {
  # Remove non-alphanumeric characters and replace with underscores
  x <- gsub("[^a-zA-Z0-9]", "_", x)
  # Convert to lowercase
  x <- tolower(x)
  # Remove any leading or trailing underscores
  x <- gsub("^_|_$", "", x)
  x <- gsub("__", "_", x)
  x <- gsub("NLCD_Land_", "", x)
  return(x)
}

r.df[, NLCD_Land := factor(clean.levels(levels(NLCD_Land))[NLCD_Land])]

# Convert factor columns to dummy variables
df.dummies <- data.table(model.matrix(~ . - 1, data = r.df[, .(NLCD_Land, state)])) %>%
  cbind(r.df[, -which(names(r.df) %in% c("NLCD_Land", "state")), with=F]) 

names(df.dummies) <- gsub("NLCD_Land", "", names(df.dummies))

# Ensure that there is more than one value per column (remove otherwise)
uniq.1 <- t( df.dummies[, lapply(.SD, uniqueN)]) %>%
  as.data.frame() %>%
  filter(V1 == 1) %>%
  row.names()

if (length(uniq.1) >= 1) {
  df.dummies <- df.dummies[, -which(names(df.dummies) %in% uniq.1), with=F]
}


pca.fit <- PCA(df.dummies, graph=F)
plot.PCA(pca.fit, choix="var")

res <- pca.fit$var$coord %>%
  as.data.frame() %>%
  mutate(var=as.factor(rownames(.))) %>%
  select(var, everything()) %>%
  as_tibble()
rownames(res) <- NULL
  
p.d1 <- ggplot(res, aes(x = reorder(var, Dim.1), y = Dim.1)) +
  geom_bar(stat = "identity", fill="darkblue") +
  coord_flip() +  # Makes it a horizontal bar chart
  labs(title = "Variable importance for Dim.1", y = "Importance", x = "Variable") +
  theme_minimal()

p.d2 <- ggplot(res, aes(x = reorder(var, Dim.2), y = Dim.2)) +
  geom_bar(stat = "identity", fill="darkred") +
  coord_flip() +  # Makes it a horizontal bar chart
  labs(title = "Variable importance for Dim.2", y = "Importance", x = "Variable") +
  theme_minimal()

ggpubr::ggarrange(plotlist=list(p.d1, p.d2), nrow=2, ncol=1)

Factor Analysis for Mixed Data

famd.fit <- FAMD(r.df, graph=F)

ggpubr::ggarrange(plotlist=purrr::map(
  c("var", "quanti", "quali"), 
  ~plot.FAMD(famd.fit, choix=.x)),
  ncol=1, nrow=3)

res <- famd.fit$var$coord %>%
  as.data.frame() %>%
  mutate(var=as.factor(rownames(.))) %>%
  select(var, everything()) %>%
  as_tibble()
rownames(res) <- NULL
  
p.d1 <- ggplot(res, aes(x = reorder(var, Dim.1), y = Dim.1)) +
  geom_bar(stat = "identity", fill="darkblue") +
  coord_flip() +  # Makes it a horizontal bar chart
  labs(title = "Variable importance for Dim.1", y = "Importance", x = "Variable") +
  theme_minimal()

p.d2 <- ggplot(res, aes(x = reorder(var, Dim.2), y = Dim.2)) +
  geom_bar(stat = "identity", fill="darkred") +
  coord_flip() +  # Makes it a horizontal bar chart
  labs(title = "Variable importance for Dim.2", y = "Importance", x = "Variable") +
  theme_minimal()

ggpubr::ggarrange(plotlist=list(p.d1, p.d2), nrow=2, ncol=1)

Pseudo-Absence Generation

First, get all of the grid-cell geometries (based on the resolution of the rasters) as spatial dataframes. Also extract each cell’s centroid, and row/column index from the original raster.

# Function to compute bounding box from centroid
compute.bbox <- function(x, y, half.res.x, half.res.y) {
  c(x - half.res.x, x + half.res.x, y - half.res.y, y + half.res.y)
}

# Function to generate a single POLYGON from the bounding box coordinates
make.polygon <- function(xmin, ymin, xmax, ymax) {
  m <- matrix(c(xmin, ymin, 
                xmax, ymin, 
                xmax, ymax, 
                xmin, ymax, 
                xmin, ymin), 
              ncol = 2, byrow = T)
  st_polygon(list(m))
}

get.grid.geoms <- function(r, .crs=NULL) {
  
  # Calculate the centroids of each cell
  centroids <- terra::xyFromCell(r, seq_len(ncell(r)))
  # Get resolution / 2 for x & y
  half.res.x <- res(r)[1] / 2
  half.res.y <- res(r)[2] / 2
  # Compute bounding box for each centroid
  bboxes <- t(apply(centroids, 1, function(pt) {
    compute.bbox(pt[1], pt[2], half.res.x, half.res.y)
  }))
  # Create dataframe
  dt <- as.data.table(bboxes)
  colnames(dt) <- c("xmin", "xmax", "ymin", "ymax")
  
  dt[, `:=` (
    # Add centroid lat/lon values to dataframe
    lon=centroids[, 1],
    lat=centroids[, 2],
    # Add i (row) and j (column) indices
    i=rowFromCell(r, 1:ncell(r)),
    j=colFromCell(r, 1:ncell(r))
  )]
  
  
  # Convert centroids to spatial points
  dt[, centroid := purrr::map2(lon, lat, ~st_point(cbind(.x, .y)))]
  
  # Create bounding box polygons for all rows and assign to geometry column
  dt[, bbox := purrr::pmap(.l=list(xmin, ymin, xmax, ymax), 
                               .f=make.polygon)]
  # Make spatial frame
  df <- st_sf(dt, 
              bbox = st_sfc(dt$bbox, crs=st_crs(r)),
              centroid = st_sfc(dt$centroid, crs=st_crs(r)))

  # Update CRS
  if (!is.null(.crs)) {
    df <- st_transform(df, st_crs(.crs)) %>%
      st_set_geometry("centroid") %>%
      st_transform(st_crs(.crs)) %>%
      st_set_geometry("bbox")
  }
  
  df %>% select(i, j, bbox, centroid)
}

.grids <- purrr::map(r.list, ~get.grid.geoms(.x, .crs=4326))
names(.grids) <- states

# See sample grid dataframe
.grids$NC

# TODO: Select pseudo-absence points

Train/Test Splitting

TODO: Include pseudo-absence data

stratified.split.idx <- function(df, p=0.7, lat.lon.bins=25) {
  # Cut along lat/lon values to create grids (lat.bin & lon.bin)
  # lat.lon.bins is the number of divisions you want
  df$lat.bin <- cut(df$lat, breaks=lat.lon.bins, labels = F)
  df$lon.bin <- cut(df$lon, breaks=lat.lon.bins, labels = F)
  
  # Create a new variable combining the stratification variables
  df %>%
    mutate(strata = paste(lat.bin, lon.bin, common.name, state)) %>%
    pull(strata) %>%
    # Create the data partitions
    createDataPartition(., p = p, list = F) %>%
    suppressWarnings()
}

prepare.data <- function(df, p=.7, lat.lon.bins=25) {
  train.index <- stratified.split.idx(df, p=p, lat.lon.bins = lat.lon.bins)
  df.train <- df[train.index, ]
  df.test <- df[-train.index, ]
  
  list(train = df.train, 
       test = df.test,
       index = train.index)
}

train.test <- prepare.data(df, .7)
train <- df[train.test$index,]
test <- df[-train.test$index,]

EDA With Pseudo-Absence Data

Autocorrelation Mitigation

Feature Engineering

Land Cover Hierarchical Updates to Categories

Each of the 20 different Land Cover Categories falls under a “parent” category (see National Land Cover Database Class Legend and Description).

Feature Selection